Papers
13
Total Citations
159
H-Index
7
About
Yanmei Jiao is a robotics researcher whose work spans robot programming, visual localization, and sensor fusion for autonomous systems. With a body of work accumulating over 150 citations, Jiao has established herself as a significant contributor to the challenge of making robots more adaptable and reliable in complex, real-world environments. Her early landmark contribution, the Multimodal Assembly Skill Decoding (MASD) system (2018, 39 citations), advanced the field of programming by demonstration, enabling industrial robots to learn assembly tasks directly from human examples rather than explicit code. This work marked an important step toward more intuitive human-robot interaction. Jiao subsequently turned her attention to visual localization, developing robust methods such as 2-Entity RANSAC (2019–2020, 40 combined citations) that handle the persistent challenge of environmental and perspective changes disrupting feature matching in autonomous navigation. More recently, she has pushed the boundaries of state estimation through multi-sensor fusion frameworks, including FEJ-VIRO (2022, 21 citations), which reduces localization drift in visual-inertial odometry, and DAMS-LIO (2023, 13 citations), a degeneration-aware LiDAR-inertial system designed for harsh environments such as underground mines. Across her career, Jiao's research consistently bridges theoretical rigor with practical deployment, making her work particularly valuable to researchers working on autonomous mobile robots and intelligent manufacturing systems.
Research Focus
Key Achievements
Top Papers
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- 4Deep Samplable Observation Model for Global Localization and Kidnapping17 citations · 2021
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- 62-Entity RANSAC for robust visual localization in changing environment11 citations · 2019
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